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Fundamentals and Applications Related to data science, Machine Learning, and Statistical Processing

Zairyo/Journal of the Society of Materials science, Japan 2024年第7期73卷 618-624页

作者： Nakagawa, Masao Nomura, Yasutoshi Data Science and AI Innovation Research Promotion Center Shiga Univ. Banba Hikone522-8522 Japan College of Science and Engineering Ritsumeikan Univ. Nojihigashi Kusatsu525-8577 Japan

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Fundamentals and Applications Related to data science, Machine Learning, and Statistical Processing

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Zairyo/Journal of the Society of Materials science, Japan 2024年第8期73卷 682-688页

作者： Nomura, Yasutoshi Nakagawa, Masao College of Science and Engineering Ritsumeikan Univ. Nojihigashi Kusatsu525-8577 Japan Data Science and AI Innovation Research Promotion Center Shiga Univ. Banba Hikone522-8522 Japan

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DASU - Transfer Center for Digitalization, Analytics & data science Ulm: Intelligent transfer strategies for the support of digital transformation

DASU - Transfer Center for Digitalization, Analytics & Data ...

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2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Von Schwerin, Reinhold Hafner, Alexander Technische Hochschule Ulm Albert-Einstein-Allee 53-55 Ulm89081 Germany DASU-Transferzentrum för Digitalisierung Analytics & Data Science Ulm Ensingerstr. 4 Ulm89073 Germany

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Using Design science Research to Orchestrate Developing the iCanViz Cancer Care data Analytics Research Project

Using Design Science Research to Orchestrate Developing the ...

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2023 International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Rub, Reema Abu Al Atiani, Serin Mohammed, Mahmoud Kharbat, Faten F Odeh, Mohammad Princess Sumaya University for Technology Data Science Department Amman Jordan Al Ain University College of Engineering Abu Dhabi United Arab Emirates University of the West of England United Kingdom Global Academy for Digital Health United Kingdom

ISBN: (纸本)9798350339291

This paper aims to reflect on the extent to which the Design science Research Method (DSRM) is aligned with orchestrating the development of cancer care data analytics research projects using a data strategy-enabled use-case-driven approach. A fit-for-purpose DSRM research framework has been designed to guide the research design and project-manage the iCanViz research project. The iCanViz project is an intelligent cancer data analytics visualizer aligned to multiple cancer sites, cancer incidences, risk correlations, survivals, and deaths in the Arab world within certain years of reported cancer incidences per cancer site. The DSRM approach has been deemed closely aligned with the iCanViz research project. This is primarily due to its ability to articulate and incorporate cancer care data strategy use-cases as agile software development increments within the iCanViz DSRM research framework. Also, the adaptation of the iCanViz DSRM research framework has contributed to enhancing the even distribution and concurrent execution of cancer care data analytics tasks across the software development and research team. Furthermore, the iCanViz DSRM research design has proven effective in designing research frameworks that bridge the gap between the world of cancer care and the creation of cancer care data analytics systems. With a small research team, the iCanViz project achieved this within a relatively short time frame of less than a year. © 2023 IEEE.

关键词： data Analytics

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Foundational data science Training for Health Equity Researchers at Minority Serving Institutions: A SoRDS Event 11

Foundational Data Science Training for Health Equity Researc...

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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Quick, Robert Córdoba, Marcela Alfaro Diggs, Stephen Cobe, Raphael Bezuidenhout, Louise Shannahan, Hugh Peterson, Bianca Indiana University Pervasive Technology Institute BloomingtonIN United States University of California Santa Cruz Dept of Statistics Santa Cruz United States California Digital Library OaklandCA United States São Paulo State University Center for Scientific Computing São Paulo Brazil Data Archiving and Networked Services The Hague Netherlands Royal Holloway University of London Department of Computer Science Egham United Kingdom Fathom Data Potchefstroom South Africa

ISBN: (纸本)9798350302639

Contemporary research, particularly when addressing the most significant transdisciplinary research challenges, cannot effectively be done without a range of skills relating to data management, data analysis, and cyberinfrastructure (CI). These data and CI skills are common to all disciplines that conduct data-centric research. Research data science acts as a vital component of the scientific process. In a grassroots attempt to address this gap, the COdata-RDA Schools of Research data science (SoRDS) was founded in 2016 to provide instruction on foundational data science and open research concepts to early career researchers in low and middle-income countries. This partnership between international collaborators has since 2016 provided this training to over 1000 early career researchers in 24 events in 10 countries worldwide. The most recent event was held at Georgia Institute of Technology and focused on health equity and included researchers from minority-serving institutions in the southeast United States. This paper covers the background of the SoRDS project along with organization and curriculum details. It also covers the transition of the events from a non-domain-centric curriculum to spotlighting biological and social health equity data and what we learned to make future health-related events more engaging and valuable to the attendees. It also looks toward future events that will serve international students studying health informatics and other data-centric disciplines. © 2023 IEEE.

关键词： Open data

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TAPS Responsibility Matrix: A tool for responsible data science by design

arXiv

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arXiv 2023年

作者： Urovi, Visara Celebi, Remzi Sun, Chang Rieswijk, Linda Erard, Michael Yilmaz, Arif Moodley, Kody Kumar, Parveen Dumontier, Michel Institute of Data Science Maastricht University P.O. Box 616 Maastricht6200 MD Netherlands

data science is an interdisciplinary research area where scientists are typically working with data coming from different fields. When using and analyzing data, the scientists implicitly agree to follow standards, procedures, and rules set in these fields. However, guidance on the responsibilities of the data scientists and the other involved actors in a data science project is typically missing. While literature shows that novel frameworks and tools are being proposed in support of open-science, data reuse, and research data management, there are currently no frameworks that can fully express responsibilities of a data science project. In this paper, we describe the Transparency, Accountability, Privacy, and Societal Responsibility Matrix (TAPS-RM) as framework to explore social, legal, and ethical aspects of data science projects. TAPS-RM acts as a tool to provide users with a holistic view of their project beyond key outcomes and clarifies the responsibilities of actors. We map the developed model of TAPS-RM with well-known initiatives for open data (such as FACT, FAIR and datasheets for datasets). We conclude that TAPS-RM is a tool to reflect on responsibilities at a data science project level and can be used to advance responsible data science by *** Codes I.2.1 © 2023, CC BY.

关键词： Transparency

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Application of data science and Artificial Intelligence in Financial decisions: A survey

Application of Data Science and Artificial Intelligence in F...

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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Shukla, Ravindra Nath Vyas, Vishal Roy, Ankur Chaturvedi, Animesh ABV-IIITM Department of Management Studies Gwalior India Management Development Institute Mdi Gurgaon India Indian Institute of Information Technology Dharwad Data Science & Intelligent Systems India

ISBN: (纸本)9781665477192

This paper provides a survey on the application of data science and Artificial Intelligence (DSAI) in decision-making for Financial services and Corporate firms. Firstly, we review the application of DSAI in Financial service decisions. The ability of DSAI models to capture the linear and nonlinear behavior of time-series data helps the development of DSAI applications in Financial Services. Secondly, we review the application of DSAI in Corporate finance decisions, which is found to be in growing stages. In Corporate finance decisions, the researchers have developed hybrid models consisting of DSAI and econometrics techniques. The success of hybrid models suggests that applying DSAI along with econometric models improves the efficiency of Corporate finance decision-making. © 2022 IEEE.

关键词： Artificial intelligence

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Enabling data-driven collaborative and reproducible environmental synthesis science

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Methods in Ecology and Evolution 2025年第6期16卷 1061-1074页

作者： Brun, Julien Lyon, Nicholas J. Chen, Angel Slette, Ingrid De La Rosa, Gabriel Caselle, Jennifer E. Davis, Frank W. Downs, Martha R. Research Data Services Library University of California Santa Barbara Santa Barbara CA United States National Center for Ecological Analysis and Synthesis University of California Santa Barbara CA United States Department of Ecology Evolution and Behavior University of Minnesota St Paul MN United States Marine Science Institute University of California Santa Barbara CA United States Bren School of Environmental Science and Management University of California Santa Barbara CA United States

This manuscript shares the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data. When providing scientific computing support, focus is first placed on how to foster the collaborative aspects of multidisciplinary projects on the technological side by providing virtual spaces to communicate and share documents. Then insights on data management planning and how to implement a centralized data management workflow for data-driven projects are provided. Developing reproducible workflows requires the development of code. We describe tools and practices that have been successful in fostering collaborative coding and scaling on remote servers, enabling teams to iterate more efficiently. We have found short training sessions combined with on-demand specialized support to be the most impactful combination in helping scientists develop their technical skills. Here we share our experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long-lasting effects on the way researchers conduct science. We hope that other groups supporting team- and data-driven science (in environmental science and beyond) will benefit from the lessons we have learned over the years through trial and error. © 2025 The Author(s). Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

关键词： collaborative science data management environmental data science reproducible science synthesis science

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A Survey on Large Language Model-based Agents for Statistics and data science

arXiv

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arXiv 2024年

作者： Sun, Maojun Han, Ruijian Jiang, Binyan Qi, Houduo Sun, Defeng Yuan, Yancheng Huang, Jian Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong

In recent years, data science agents powered by Large Language Models (LLMs), known as "data agents," have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lowering the entry barrier for users without related expertise. We explore current trends in the design of LLM-based frameworks, detailing essential features such as planning, reasoning, reflection, multi-agent collaboration, user interface, knowledge integration, and system design, which enable agents to address data-centric problems with minimal human intervention. Furthermore, we analyze several case studies to demonstrate the practical applications of various data agents in real-world scenarios. Finally, we identify key challenges and propose future research directions to advance the development of data agents into intelligent statistical analysis software. © 2024, CC BY.

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Deep and shallow data science for multi-scale optical neuroscience

arXiv

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arXiv 2024年

作者： Mishne, Gal Charles, Adam Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering the Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically relevant information. In this pursuit, challenges arise in some domains (e.g., SNR and resolution limits in micron-scale data) that require specialized algorithms. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale to optimize the processing pipeline. In contrast, other methods, however, such as graph signal processing, seek to abstract away from some of the details that are scale-specific to provide solutions to specific sub-problems common across scales of neuroimaging. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination. © 2024, CC BY.

关键词： Neuroimaging

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